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Managing natural resources in wide-scale areas can be highly time and resource consuming task which requires significant amount of data collection in the field and reduction of the data in the office to provide the necessary information. High performance LiDAR remote sensing technology has recently become an effective tool for use in applications of natural resources. In the field of forestry, the LiDAR measurements of the forested areas can provide high-quality data on three-dimensional characterizations of forest structures. Besides, LiDAR data can be used to provide very high quality and accurate Digital Elevation Model (DEM) for the forested areas. This study presents the progress and opportunities of using LiDAR remote sensing technology in various forestry applications. The results indicate that LiDAR based forest structure data and high-resolution DEMs can be used in wide-scale forestry activities such as stand characterizations, forest inventory and management, fire behaviour modeling, and forest operations.

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We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an evacuee by considering his/her individual features. SmartEscape, which is fast, low-cost, low resource-consuming and mobile supported, collects various environmental sensory data and takes evacuees' individual features into account, uses an artificial neural network (ANN) to calculate personal usage risk of each link in the building, eliminates the risky ones, and calculates an optimum escape route under existing circumstances. Then, our system guides the evacuee to the exit through the calculated route with vocal and visual instructions on the smartphone. While the position of the evacuee is detected by RFID (Radio-Frequency Identification) technology, the changing environmental conditions are measured by the various sensors in the building. Our ANN (Artificial Neural Network) predicts dynamically changing risk states of all links according to changing environmental conditions. Results show that SmartEscape, with its 98.1% accuracy for predicting risk levels of links for each individual evacuee in a building, is capable of evacuating a great number of people simultaneously, through the shortest and the safest route.

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In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth-generation (5G) and beyond. The proposed scheme adopts unique signatures depicted in two-dimensional asynchronously sampled in-phase-quadrature amplitudes' histograms (2D-ASIQHs)-based images and applies the support vector machines (SVMs) tool. Along with the estimation of the instantaneous SNR values over 0-35 dB range, the determination of nine modulation types that belong to different modulation categories i.e., phase-shift keying (Binary-PSK, Quadrature-PSK, and 8-PSK), amplitude-shift keying (2-ASK and 4-ASK) and quadrature-amplitude modulation (4-QAM, 16-QAM, 32-QAM, and 64-QAM) could be achieved by this scheme. The application of this scheme has been simulated using a channel model that is impaired by additive white Gaussian noise (AWGN) and Rayleigh fading, covering a broad range of SNRs of 0-35 dB. The performance of this dual-determination scheme shows high modulation recognition accuracy and low mean SNR estimation error. Therefore, it can be a better alternative for designers of next generation wireless communication systems.

Water, in any way it comes, is important for the life of all living things. Indonesia is an area of tropical equatorial with a variation of rain, which is quite high. The regularity of the distribution of rainfall is one of the aspects most important to the activity of the community. As the development of technology, the intensity of rainfall can be measured manually using Ombrometer Observatory tool. The manual tool for measuring the rain precipitation, Ombrometer Observatorium, is used to take data manually. Samples should be taken at 7.00 a.m. everyday using a measuring cup to know the height of the water contained. However, the type is prone to error at the high rainfall intensity, since the drainage of the samples is conducted every 24 hours. Therefore, much water is wasted. To solve the problem, a modification of a rainfall gauge was made, that is Ombrometer Observatory with ultrasonic sensor HC-SR04. The height of the water in the container is sent through a server of which the data is stored in the database every ten minutes to reduce the risk of evaporation. It also minimizes the error in measuring the rainfall intensity. The results have been compared to the ones by BMKG (Meteorology, Climatology, and Geophysics Agency). The correlation value of the measurement ratio reached 0.9739 or 97.39%.

Abstract-In this paper, we propose a road detection model approach based on neural networks from satellite images. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. According the neural network, the RGB values are used for deciding the pixel belongs to road or not. The found road pixels are marked in the output image.